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Comparative modelling of interregional transport flows: Applications to multimodal European freight transport

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  • Nijkamp, Peter
  • Reggiani, Aura
  • Tsang, Wai Fai
Abstract
This paper aims to compare the descriptive and predictive power of two classes of statistical estimation models for multimodal network flows, viz. the logit model and the neural network model. The application concerns a large data set on inter-regional European freight flows for two commodity categories (food and chemicals). After an exposition of policy issues, methodological and modelling questions and the database, a variety of experiments is carried out. The results show that in general the predictive potential of neural network models is higher than that of logit analysis. The statistical results are also used to investigate the implications of various road tax systems (e.g., e&axes) on various segments of the European road network
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Suggested Citation

  • Nijkamp, Peter & Reggiani, Aura & Tsang, Wai Fai, 2004. "Comparative modelling of interregional transport flows: Applications to multimodal European freight transport," European Journal of Operational Research, Elsevier, vol. 155(3), pages 584-602, June.
    • Handle: RePEc:eee:ejores:v:155:y:2004:i:3:p:584-602
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    1. Giovanni Russo & Aura Reggiani & Peter Nijkamp, 2001. "Modelling and Estimating Modal Share in European Transport," Tinbergen Institute Discussion Papers 01-095/3, Tinbergen Institute.
    2. Erik Verhoef, 1996. "The Economics of Regulating Road Transport," Books, Edward Elgar Publishing, number 939.
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    4. Yang, Hai & Kitamura, Ryuichi & Jovanis, Paul P. & Vaughn, Kenneth M. & Abdel-aty, Mohammed A. & Reddy, Prasuna Dvg, 1993. "Exploration Of Driver Route Choice With Advanced Traveler Information Using Neural Network Concepts," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt53d2t6df, Institute of Transportation Studies, UC Berkeley.
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    6. Hensher, David A. & Ton, Tu T., 2000. "A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(3), pages 155-172, September.
    7. Aura Reggiani (ed.), 2000. "Spatial Economic Science," Advances in Spatial Science, Springer, number 978-3-642-59787-9, February.
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    Cited by:

    1. Ahmet Kubas & I. Inan & Gokhan Unakitan & E. Erbay, 2008. "The Estimation of the Relationships between Water-Natural Gas Usage and Discharge-Emission Permission by Using Binary Logistic Model for the Industrial Establishments," Quality & Quantity: International Journal of Methodology, Springer, vol. 42(1), pages 35-44, February.
    2. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
    3. Carlos Llano & Almudena Esteban & Julian Pérez & Antonio Pulido, 2010. "Opening the Interregional Trade ‘‘Black Box’’: The C-Intereg Database for the Spanish Economy (1995—2005)," International Regional Science Review, , vol. 33(3), pages 302-337, July.
    4. Zhao, Laijun & Zhao, Yue & Hu, Qingmi & Li, Huiyong & Stoeter, Johan, 2018. "Evaluation of consolidation center cargo capacity and loctions for China railway express," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 117(C), pages 58-81.
    5. Ana L.M. Sargento & Pedro Nogueira Ramos & Geoffrey J.D. Hewings, 2012. "Inter-Regional Trade Flow Estimation Through Non-Survey Models: An Empirical Assessment," Economic Systems Research, Taylor & Francis Journals, vol. 24(2), pages 173-193, March.
    6. Marialisa Nigro & Marina Ferrara & Rosita De Vincentis & Carlo Liberto & Gaetano Valenti, 2021. "Data Driven Approaches for Sustainable Development of E-Mobility in Urban Areas," Energies, MDPI, vol. 14(13), pages 1-19, July.
    7. Tao, Xuezong & Zhu, Lichao, 2020. "Meta-analysis of value of time in freight transportation: A comprehensive review based on discrete choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 213-233.
    8. Albert, Adrian & Schaefer, Andreas, 2013. "Demand for freight transportation in the U.S.: a high-level view," 54th Annual Transportation Research Forum, Annapolis, Maryland, March 21-23, 2013 206946, Transportation Research Forum.
    9. Lucia Rotaris & Romeo Danielis & Igor Sarman & Edoardo Marcucci, 2012. "Testing for nonlinearity in the choice of a freight transport service," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 50, pages 1-4.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General

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